OpenRaiser/Intern-Atlas huggingface dataset:
Game theoretic pricing policy in online media delivery platforms
Venue: arXiv cs.NI 2019
Year: 2019

Residential users get most of their preferred content (e.g., films, news) from their wireless access point, which is connected to content providers through one or several network providers. Nowadays, these access points have enough storage and wireless transmission capacities to distribute the content to neighboring access points if required and allowed by the user. Based on this concept, this paper considers a hybrid content distribution model in peer-to-peer content deliver networks. A user or peer in this network can get its content either directly from the content provider (CP), or from a neighboring user who has this content stored and is willing to share it. Two types of users are considered: premium users are the users getting always the content directly from the CP, and standard user are the users getting the content from other standard users, when possible, otherwise from the CP. Furthermore, standard user share their content to other users if required. This paper studies the pricing problem of the CP such that there are enough standard users to distribute the content (using less network resources and reducing significantly the delay) while maximizing the CP benefits.We address the problem from a game theoretic approach and evaluate when the Nash Equilibrium can be attained. The obtained case have been applied to a real life study using a BBC iPlayer dataset and an analysis on the CP benefits dependence on different parameters. Finally we extend this pricing model when considering also the reduction of load on its content custodian (server) and discuss how the pricing policy is affected.
Solving Visual Object Ambiguities when Pointing: An Unsupervised Learning Approach
Venue: arXiv cs.CV 2019
Year: 2019

Whenever we are addressing a specific object or refer to a certain spatial location, we are using referential or deictic gestures usually accompanied by some verbal description. Especially pointing gestures are necessary to dissolve ambiguities in a scene and they are of crucial importance when verbal communication may fail due to environmental conditions or when two persons simply do not speak the same language. With the currently increasing advances of humanoid robots and their future integration in domestic domains, the development of gesture interfaces complementing human-robot interaction scenarios is of substantial interest. The implementation of an intuitive gesture scenario is still challenging because both the pointing intention and the corresponding object have to be correctly recognized in real-time. The demand increases when considering pointing gestures in a cluttered environment, as is the case in households. Also, humans perform pointing in many different ways and those variations have to be captured. Research in this field often proposes a set of geometrical computations which do not scale well with the number of gestures and objects, use specific markers or a predefined set of pointing directions. In this paper, we propose an unsupervised learning approach to model the distribution of pointing gestures using a growing-when-required (GWR) network. We introduce an interaction scenario with a humanoid robot and define so-called ambiguity classes. Our implementation for the hand and object detection is independent of any markers or skeleton models, thus it can be easily reproduced. Our evaluation comparing a baseline computer vision approach with our GWR model shows that the pointing-object association is well learned even in cases of ambiguities resulting from close object proximity.
Relative planar motion for vehicle-mounted cameras from a single affine correspondence
Venue: arXiv cs.CV 2019
Year: 2019

Two solvers are proposed for estimating the extrinsic camera parameters from a single affine correspondence assuming general planar motion. In this case, the camera movement is constrained to a plane and the image plane is orthogonal to the ground. The algorithms do not assume other constraints, e.g.\ the non-holonomic one, to hold. A new minimal solver is proposed for the semi-calibrated case, i.e. the camera parameters are known except a common focal length. Another method is proposed for the fully calibrated case. Due to requiring a single correspondence, robust estimation, e.g. histogram voting, leads to a fast and accurate procedure. The proposed methods are tested in our synthetic environment and on publicly available real datasets consisting of videos through tens of kilometres. They are superior to the state-of-the-art both in terms of accuracy and processing time.
Implementing a Protocol Native Managed Cryptocurrency
Venue: arXiv cs.CR 2019
Year: 2019

Previous work presented a theoretical model based on the implicit Bitcoin specification for how an entity might issue a protocol native cryptocurrency that mimics features of fiat currencies. Protocol native means that it is built into the blockchain platform itself and is not simply a token running on another platform. Novel to this work were mechanisms by which the issuing entity could manage the cryptocurrency but where their power was limited and transparency was enforced by the cryptocurrency being implemented using a publicly mined blockchain. In this work we demonstrate the feasibility of this theoretical model by implementing such a managed cryptocurrency architecture through forking the Bitcoin code base. We discovered that the theoretical model contains several vulnerabilities and security issues that needed to be mitigated. It also contains architectural features that presented significant implementation challenges; some aspects of the proposed changes to the Bitcoin specification were not practical or even workable. In this work we describe how we mitigated the security vulnerabilities and overcame the architectural hurdles to build a working prototype.
Founding The Domain of AI Forensics
Venue: arXiv cs.CR 2019
Year: 2019

With the widespread integration of AI in everyday and critical technologies, it seems inevitable to witness increasing instances of failure in AI systems. In such cases, there arises a need for technical investigations that produce legally acceptable and scientifically indisputable findings and conclusions on the causes of such failures. Inspired by the domain of cyber forensics, this paper introduces the need for the establishment of AI Forensics as a new discipline under AI safety. Furthermore, we propose a taxonomy of the subfields under this discipline, and present a discussion on the foundational challenges that lay ahead of this new research area.
Resolving Conflicts and Electing Leaders
Venue: arXiv cs.DC 2019
Year: 2019

We review distributed algorithms for transmitting data ($n$ real numbers) under a broadcast communication model, as well as for maximum finding and for sorting. Our interest is in the basics of recursive formulas and corresponding asymptotics as ${n\to\infty}$. The emphasis is on concrete examples rather than general theory.
Challenges in designing edge-based middlewares for the Internet of Things: A survey
Venue: arXiv cs.DC 2019
Year: 2019

The Internet of Things paradigm connects edge devices via the Internet enabling them to be seamlessly integrated with a wide variety of applications. In recent years, the number of connected devices has grown significantly, along with the volume and variety of data that is being generated by these devices at the edge of the network. An edge-based middleware is defined as a software that serves as an interface between the computational resources and the IoT devices, making communication possible among elements. Such middleware is required to provide the necessary functional components for sensor registration, discovery, workflow composition, and data pre-processing. In recent years, the landscape of the edge middleware platforms has grown exponentially, each of them with different platform requirements, architectures, and features. The core of this survey is a comprehensive review of existing edge middleware solutions. In this regard, we propose a four-layer architecture for the design of edge-based middleware, along with some design goals for each of the proposed layer. The paper concludes with some open challenges and possible future research directions.
Active learning in the geometric block model
Venue: arXiv cs.SI 2019
Year: 2019

The geometric block model is a recently proposed generative model for random graphs that is able to capture the inherent geometric properties of many community detection problems, providing more accurate characterizations of practical community structures compared with the popular stochastic block model. Galhotra et al. recently proposed a motif-counting algorithm for unsupervised community detection in the geometric block model that is proved to be near-optimal. They also characterized the regimes of the model parameters for which the proposed algorithm can achieve exact recovery. In this work, we initiate the study of active learning in the geometric block model. That is, we are interested in the problem of exactly recovering the community structure of random graphs following the geometric block model under arbitrary model parameters, by possibly querying the labels of a limited number of chosen nodes. We propose two active learning algorithms that combine the idea of motif-counting with two different label query policies. Our main contribution is to show that sampling the labels of a vanishingly small fraction of nodes (sub-linear in the total number of nodes) is sufficient to achieve exact recovery in the regimes under which the state-of-the-art unsupervised method fails. We validate the superior performance of our algorithms via numerical simulations on both real and synthetic datasets.
LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Semantic image segmentation plays a pivotal role in many vision applications including autonomous driving and medical image analysis. Most of the former approaches move towards enhancing the performance in terms of accuracy with a little awareness of computational efficiency. In this paper, we introduce LiteSeg, a lightweight architecture for semantic image segmentation. In this work, we explore a new deeper version of Atrous Spatial Pyramid Pooling module (ASPP) and apply short and long residual connections, and depthwise separable convolution, resulting in a faster and efficient model. LiteSeg architecture is introduced and tested with multiple backbone networks as Darknet19, MobileNet, and ShuffleNet to provide multiple trade-offs between accuracy and computational cost. The proposed model LiteSeg, with MobileNetV2 as a backbone network, achieves an accuracy of 67.81% mean intersection over union at 161 frames per second with $640 \times 360$ resolution on the Cityscapes dataset.
Long-Term Planning and Situational Awareness in OpenAI Five
Venue: arXiv cs.CL 2019
Year: 2019

Understanding how knowledge about the world is represented within model-free deep reinforcement learning methods is a major challenge given the black box nature of its learning process within high-dimensional observation and action spaces. AlphaStar and OpenAI Five have shown that agents can be trained without any explicit hierarchical macro-actions to reach superhuman skill in games that require taking thousands of actions before reaching the final goal. Assessing the agent's plans and game understanding becomes challenging given the lack of hierarchy or explicit representations of macro-actions in these models, coupled with the incomprehensible nature of the internal representations. In this paper, we study the distributed representations learned by OpenAI Five to investigate how game knowledge is gradually obtained over the course of training. We also introduce a general technique for learning a model from the agent's hidden states to identify the formation of plans and subgoals. We show that the agent can learn situational similarity across actions, and find evidence of planning towards accomplishing subgoals minutes before they are executed. We perform a qualitative analysis of these predictions during the games against the DotA 2 world champions OG in April 2019.
Learning to Wait: Wi-Fi Contention Control using Load-based Predictions
Venue: arXiv cs.NI 2019
Year: 2019

We propose and experimentally evaluate a novel method that dynamically changes the contention window of access points based on system load to improve performance in a dense Wi-Fi deployment. A key feature is that no MAC protocol changes, nor client side modifications are needed to deploy the solution. We show that setting an optimal contention window can lead to throughput and latency improvements up to 155%, and 50%, respectively. Furthermore, we devise an online learning method that efficiently finds the optimal contention window with minimal training data, and yields an average improvement in throughput of 53-55% during congested periods for a real traffic-volume workload replay in a Wi-Fi test-bed.
Bayesian Linear Regression on Deep Representations
Venue: arXiv cs.LG 2019
Year: 2019

A simple approach to obtaining uncertainty-aware neural networks for regression is to do Bayesian linear regression (BLR) on the representation from the last hidden layer. Recent work [Riquelme et al., 2018, Azizzadenesheli et al., 2018] indicates that the method is promising, though it has been limited to homoscedastic noise. In this paper, we propose a novel variation that enables the method to flexibly model heteroscedastic noise. The method is benchmarked against two prominent alternative methods on a set of standard datasets, and finally evaluated as an uncertainty-aware model in model-based reinforcement learning. Our experiments indicate that the method is competitive with standard ensembling, and ensembles of BLR outperforms the methods we compared to.
Approximations in Probabilistic Programs
Venue: arXiv cs.PL 2019
Year: 2019

We study the first-order probabilistic programming language introduced by Staton et al. (2016), but with an additional language construct, $\mathbf{stat}$, that, like the fixpoint operator of Atkinson et al. (2018), converts the description of the Markov kernel of an ergodic Markov chain into a sample from its unique stationary distribution. Up to minor changes in how certain error conditions are handled, we show that $\mathbf{norm}$ and $\mathbf{score}$ are eliminable from the extended language, in the sense of Felleisen (1991). We do so by giving an explicit program transformation and proof of correctness. In fact, our program transformation implements a Markov chain Monte Carlo algorithm, in the spirit of the "Trace-MH" algorithm of Wingate et al. (2011) and Goodman et al. (2008), but less sophisticated to enable analysis. We then explore the problem of approximately implementing the semantics of the language with potentially nested $\mathbf{stat}$ expressions, in a language without $\mathbf{stat}$. For a single $\mathbf{stat}$ term, the error introduced by the finite unrolling proposed by Atkinson et al. (2018) vanishes only asymptotically. In the general case, no guarantees exist. Under uniform ergodicity assumptions, we are able to give quantitative error bounds and convergence results for the approximate implementation of the extended first-order language.
Follow Pedro! An Infrared-based Person-Follower using Nonlinear Optimization
Venue: arXiv cs.RO 2019
Year: 2019

We used ROS2 as a platform to conduct AI research for developing a Follow-Me capability as a proof-of-concept on a wheeled robot, demonstrating that AI research is possible in the ROS2 framework. We developed a complete system that uses perception and navigation components based on a sensor suite of fisheye cameras, lidar, and IMU running on an ARM64 Embedded Linux platform that runs ROS2 natively. The perception package detects AR markers and/or IR beacons to track a person. The tracker uses AI algorithms such as particle filters and nonlinear optimization to extract the SE(3) information of the 2D feature.
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
Venue: arXiv cs.CV 2019
Year: 2019

Satellite images hold great promise for continuous environmental monitoring and earth observation. Occlusions cast by clouds, however, can severely limit coverage, making ground information extraction more difficult. Existing pipelines typically perform cloud removal with simple temporal composites and hand-crafted filters. In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds. We train our model on a new large-scale spatiotemporal dataset that we construct, containing 97640 image pairs covering all continents. We demonstrate experimentally that the proposed STGAN model outperforms standard models and can generate realistic cloud-free images with high PSNR and SSIM values across a variety of atmospheric conditions, leading to improved performance in downstream tasks such as land cover classification.
Region and Object based Panoptic Image Synthesis through Conditional GANs
Venue: arXiv cs.CV 2019
Year: 2019

Image-to-image translation is significant to many computer vision and machine learning tasks such as image synthesis and video synthesis. It has primary applications in the graphics editing and animation industries. With the development of generative adversarial networks, a lot of attention has been drawn to image-to-image translation tasks. In this paper, we propose and investigate a novel task named as panoptic-level image-to-image translation and a naive baseline of solving this task. Panoptic-level image translation extends the current image translation task to two separate objectives of semantic style translation (adjust the style of objects to that of different domains) and instance transfiguration (swap between different types of objects). The proposed task generates an image from a complete and detailed panoptic perspective which can enrich the context of real-world vision synthesis. Our contribution consists of the proposal of a significant task worth investigating and a naive baseline of solving it. The proposed baseline consists of the multiple instances sequential translation and semantic-level translation with domain-invariant content code.
Regularizing Deep Multi-Task Networks using Orthogonal Gradients
Venue: arXiv cs.LG 2019
Year: 2019

Deep neural networks are a promising approach towards multi-task learning because of their capability to leverage knowledge across domains and learn general purpose representations. Nevertheless, they can fail to live up to these promises as tasks often compete for a model's limited resources, potentially leading to lower overall performance. In this work we tackle the issue of interfering tasks through a comprehensive analysis of their training, derived from looking at the interaction between gradients within their shared parameters. Our empirical results show that well-performing models have low variance in the angles between task gradients and that popular regularization methods implicitly reduce this measure. Based on this observation, we propose a novel gradient regularization term that minimizes task interference by enforcing near orthogonal gradients. Updating the shared parameters using this property encourages task specific decoders to optimize different parts of the feature extractor, thus reducing competition. We evaluate our method with classification and regression tasks on the multiDigitMNIST, NYUv2 and SUN RGB-D datasets where we obtain competitive results.
Study of Adaptive Activity-Aware Constellation List-Based Detection for Massive Machine-Type Communications
Venue: arXiv cs.IT 2019
Year: 2019

In this work, we propose an adaptive list-based decision feedback detector along with an $l_0$-norm regularized recursive least-squares algorithm that only requires pilot symbols (AA-CL-DF). The proposed detector employs a list strategy based on the signal constellation points to generate different candidates for detection. Simulation results show that the proposed AA-CL-DF successfully mitigates the error propagation and approaches the performance for the oracle LMMSE algorithm.
Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods
Venue: arXiv cs.MA 2019
Year: 2019

In this article, we report on the efficiency and effectiveness of multiagent reinforcement learning methods (MARL) for the computation of flight delays to resolve congestion problems in the Air Traffic Management (ATM) domain. Specifically, we aim to resolve cases where demand of airspace use exceeds capacity (demand-capacity problems), via imposing ground delays to flights at the pre-tactical stage of operations (i.e. few days to few hours before operation). Casting this into the multiagent domain, agents, representing flights, need to decide on own delays w.r.t. own preferences, having no information about others' payoffs, preferences and constraints, while they plan to execute their trajectories jointly with others, adhering to operational constraints. Specifically, we formalize the problem as a multiagent Markov Decision Process (MA-MDP) and we show that it can be considered as a Markov game in which interacting agents need to reach an equilibrium: What makes the problem more interesting is the dynamic setting in which agents operate, which is also due to the unforeseen, emergent effects of their decisions in the whole system. We propose collaborative multiagent reinforcement learning methods to resolve demand-capacity imbalances: Extensive experimental study on real-world cases, shows the potential of the proposed approaches in resolving problems, while advanced visualizations provide detailed views towards understanding the quality of solutions provided.
Training for Channel Estimation in Nonlinear Multi-Antenna Transceivers
Venue: arXiv cs.IT 2019
Year: 2019

Recent efforts to obtain high data rates in wireless systems have focused on what can be achieved in systems that have nonlinear or coarsely quantized transceiver architectures. Estimating the channel in such a system is challenging because the nonlinearities distort the channel estimation process. It is therefore of interest to determine how much training is needed to estimate the channel sufficiently well so that the channel estimate can be used during data communication. We provide a way to determine how much training is needed by deriving a lower bound on the achievable rate in a training-based scheme that can be computed and analyzed even when the number of antennas is very large. This lower bound can be tight, especially at high SNR. One conclusion is that the optimal number of training symbols may paradoxically be smaller than the number of transmitters for systems with coarsely-quantized transceivers. We show how the training time can be strongly dependent on the number of receivers, and give an example where doubling the number of receivers reduces the training time by about 37 percent.
Utilizing Players' Playtime Records for Churn Prediction: Mining Playtime Regularity
Venue: arXiv cs.HC 2019
Year: 2019

In the free online game industry, churn prediction is an important research topic. Reducing the churn rate of a game significantly helps with the success of the game. Churn prediction helps a game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Playtime related features are some of the widely used universal features for most churn prediction models. In this paper, we consider developing new universal features for churn predictions for long-term players based on players' playtime.